In the field of railroad engineering and maintenance, proactive maintenance of rails comprising the tracks of a railroad is extremely important for insuring safe operation of trains on the tracks. Gage widening (increase in separation between left and right rails), rail wear, fatigue-induced cracks, and other conditions have the potential to cause harmful consequences such as train derailments. Therefore, state-of-art methods are used to inspect track conditions at regular intervals along geographic sections of railroad, the sections comprising the entire length of a given line.
Special track geometry measurement vehicles known in the art as “Geocars” are available to the inventor for measuring and thus enabling acquisition of important information about the condition of railway tracks along a line. Measurements that are important in proactive maintenance of a line include such as gage parameters, alignment parameters, curvature parameters, cross-level parameters, surface quality parameters, wear parameters, and so on.
Through ongoing track analysis, track degradation problems can be identified and located. By comparing old sets of data with newer sets of data along a same set of tracks, certain degradation problems can be predicted. Predicting the behavior of track degradation can be useful in planning proactive maintenance actions. Typically, analyzing and extrapolating the behavior of track measurement data taken from subsequent test vehicle runs recorded on different dates, provides maintenance authorities with information that enables one or more predictions indicative of what type of proactive maintenance should be initiated and when it should be initiated.
A requirement of the method described immediately above is that the geographic locations of measured data has to be known within a reasonable accuracy so that data from different test runs on different test dates can be compared consistently and behavior can be projected in a future sense. Some track measuring systems have geographic data provided through the use of the well-known Global Positioning System (GPS). However most existing system do not have this advantage, partly because of expense, and therefore must rely on older methods for acquiring geographic location information to aid in locating specific measured characteristics. Even with the use of GPS, geographic position results may still not be reasonable accurate for exactly pinpointing potential problems.
In the case of the systems that do not have access to GPS, the most critical problem encountered in performance of track degradation analysis is the unavailability of correct geographic location references for track measurement data recorded on different test dates. With these systems geographic location information is acquired manually by the vehicle test operator or other authorized persons by measuring distances from planted mileposts, for example. Such measurements are approximate at best. At times a geographic location assessment is made before arriving at a planned test location, or after passing the test location. An error margin of as much as 250 feet is typical in relating a geographic location to an actual test site where specific measurements were taken under such circumstances.
Other factors can cause misalignment of geographic location to test-sites, such as odometer malfunctions of a particular test vehicle and inconsistencies of odometer performance from vehicle to vehicle. For example, odometer readings are typically used to update location information for test sites. If a particular odometer of a test vehicle has a calibration error resulting in inaccurate measurement results, then the amount of error increases with distance traveled. Error in calibration can result in a standard measurement unit, for example, a foot or a meter, to be recorded longer or shorter than the actual measure. Multiplied error over distance can be as much as 50 feet misalignment in a mile or so distance. Moreover, different vehicles used to test a same length of track on different dates may have differing calibration errors, states of wheel wear, or wheel slip conditions resulting in further inconstancies. The problem can be further affected by human error. Automatic Location Detectors are available for many railroads and are used to mark and identify geographic location of track measurement data, however these detectors are often not reliably picked up by passing test vehicles and those that are detected still do not provide enough data for correct data alignment.
A system for locating a vehicle along a length of railroad track is known to the inventor and described in U.S. Pat. No. 5,791,063 hereinafter '063. This system includes pre-measuring track geometry along the length of a railroad track and then storing this information in a historical data repository. As a vehicle moves along the same length of track having a historical geometry, the vehicle creates a real-time version of the same data and then the data is compared in order to pinpoint location of the vehicle. The described method relies on GPS positioning and previously aligned track data for reference.
A similar method is also known to the inventor and is referenced in a publication (http://ece.caeds.eng.uml.edu/Faculty/Rome/rail/trbiand.pdf) and was presented at the Sixth International Heavy Haul Railway Conference—“Strategies Beyond 2000”, 6–10 Apr. 1997, Cape Town, South Africa. This known method uses an estimation technique based on an extended Kalman filter to recursively align track geometry data. The method and apparatus of the recursive system comprises an expensive turnkey system, which may in some embodiments also rely on GPS positioning. This method uses previously aligned data as a reference and cross-correlates new measured data against the reference or previously aligned data. It attempts to align the data using an extended Kalman filter based on the similarity of gage and cross-level signatures that are retained by the track over time. The method also requires previously aligned track data as a reference.
In light of the limitations in the prior art it has occurred to the inventor that a more economical solution is needed for finding correct geographic location of track measurement data through intelligently comparing it with track geography data that is already available in record. Track geography data information for tracks laid by a railroad is available, for example, as a part of a Roadway Information System (RIS) database and includes information such as curvature and super-elevation of curved portions of tracks.
Therefore, what is clearly needed is a method and apparatus that can be used to identify features in track measurement data that can be matched against those same features available in the track geography data of record. A system such as this could accurately locate detected problems and abnormalities in a large length of track in an automated fashion without reliance on historical alignment data or GPS positioning systems and therefore could be provided more economically and practically.